class_docstr: Hellinger Distance split criterion.
  
  The Hellinger distance is a measure of distributional divergence.
  It is used as the splitting criterion [^1] on decision trees to to address
  the imbalanced data problem.
  
  References
  ----------
  [^1]: Cieslak, David A., T. Ryan Hoens, Nitesh V. Chawla, and W. Philip Kegelmeyer.
  "Hellinger distance decision trees are robust and skew-insensitive." Data Mining
  and Knowledge Discovery 24, no. 1 (2012): 136-158.
code: def num_subsets_greater_than_frac(distributions, min_frac):
      total_weight = 0.0
      dist_sums = [0.0] * len(distributions)
      for i in range(len(dist_sums)):
          dist_sums[i] = sum(distributions[i].values())
          total_weight += dist_sums[i]
      num_greater = 0
      for d in dist_sums:
          if (d / total_weight) > min_frac:
              num_greater += 1
      return num_greater
  
file_path: river/river/tree/split_criterion/hellinger_distance_criterion.py
imports: from __future__ import annotations
  import math
  from .base import SplitCriterion
label_desc: Use image node_id 5 for calling the HellingerDistanceCriterion obj's underlying member method code with example usage: obj.num_subsets_greater_than_frac(distributions, min_frac) and returns: num_greater